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Jonathan Miguel Castillo Jaramillo

Machine Learning Engineer

Anyone AI

Technologies

My Portfolio Highlights

My New Workbook

Feature Engineering

My New Course

Introduction to Python

My New Workbook

Feature Engineering

Insightful puzzle solver, piecing together data fragments to reveal the big picture.

My Work

Take a look at my latest work.

DataLab

Feature Engineering

DataLab

Duplicate of Feature Engineering for Predicting Hotel Bookings with tidymodels

DataLab

Solution Feature Engineering

DataLab

Feature Engineering

DataCamp Course Completion

Take a look at all the courses I’ve completed on DataCamp.

Courses

Introduction to Python

Courses

Introduction to Python

My Work Experience

Where I've interned and worked during my career.

Anyone AI | Aug 2022 - Present

Machine learning engineer

Ml Projects: • Object Detection for In-store Inventory Management Applied computer vision techniques to verify the presence of products in thousands of images of supermarket shelves, where we built an API service that was backed by a machine learning model for object detection (YoloV5) that accepts images of shelves and returns the bounding boxes with the location of both the products and the missing products. • Movie review sentiment analysis classification. Analyze sentiment in product reviews for a movie streaming service. Manipulated data that is not in a traditional format, pre-processed it, and vectorized text data using BoW and TF-IDF. Trained a word embedding and used it as a vectorization source for the data. Trained a sentiment analysis model to detect positive and negative opinions for movie reviews. • Image Classification for E-Commerce. Predict vehicle make and model from unstructured e-commerce images. Trained on a pre-built dataset of 196 classes. Visualized and cleaned the dataset, pre-processed and augmented data, and trained a fine-grained classification model using convolutional neural networks achieving 74.8% accuracy in the prediction of make and model combined. Deployed in AWS instances using Docker, using an API-based web-service application. • Home Credit Risk Analysis Predicted whether a person applying for a home credit will be able to repay his debt or not. Manipulated and visualized data, and performed data pre-processing for a large dataset of +350,000 transactions. Trained many supervised models achieving +0.75 ROC AUC. Models used were DecisionTree, XGBoost, and LightGBM. • Salary Prediction Model The goal was to predict salary levels based on historical data for sports players. Collected and analyzed data via an API using Python and Pandas. Performed feature engineering and standardization. Selected evaluation metrics and baseline models. Trained a Logistic regression model, achieving an F1 score of 77%.

My Education

Take a look at my formal education

Full time Student | Machine Learning Developer | Artificial IntelligenceAnyone AI | 2022

Universidad Politécnica Salesiana | 2020

About Me

I am a Python developer specialized in Machine Learning and I'm open to new challenges that will allow me to grow in my career and improve skills in the Machine Learning/ Data Science field.

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